Hysteresis-Aware Neural Network Modeling and Whole-Body Reinforcement Learning Control of Soft Robots

Soft robots are inherently compliant and safe, making them suitable for humaninteractive applications such as surgery. However, their nonlinear and hysteretic behavior poses significant challenges for accurate modeling and control. We present a soft robotic system and propose a hysteresis-aware whole-body neural network model that accurately captures and predicts the soft robot’s whole-body motion, including hysteresis effects. Based on this model, we construct a highly parallel simulation environment for soft robot control and apply an on-policy reinforcement learning algorithm to efficiently train whole-body motion control policies. The trained policy is deployed on a real soft robot to evaluate its control performance, and it exhibits high precision in trajectory tracking tasks. Furthermore, we develop a soft robotic system for surgical applications and validate it through phantom-based laser ablation experiments. The results demonstrate that the proposed model significantly reduces prediction error compared to conventional methods. The overall framework shows strong performance in phantom-based surgical experiments, and demonstrates its potential for complex scenarios, including future real-world clinical applications.